Abstract: In LTE-R handover,the A3 event-based handover algorithm is prone to causing ping-pong effect and Radio Link connection Failure(RLF) when running at high speed.Therefore,the RBF neural network-based handover optimization algorithm is proposed.The algorithm collects hys and ttt parameter sets with good handover effect when the train runs at different speeds in a specific environment,and sends them to the RBF neural network for training to obtain the nonlinear expression of hys and ttt at different speed.Based on the received quality of the signal received by the train,the self-correcting term is added to perform secondary adjustment and optimization of hys and ttt.Simulation experimental results on Matlab show that the proposed algorithm reduces the call drop rate and ping-pong handover rate,and improves the handover success rate and robustness of the train in high-speed operation environment.

Key words: LTE-R technology, high-speed environment, A3 event, RBF neural network, handover success rate

摘要： 在LTE-R越区切换中，基于A3事件的越区切换算法在列车高速运行时容易出现乒乓效应和无线链路连接失败的问题。为此，提出基于RBF神经网络的越区切换优化算法。采集列车运行在特定环境中不同速度时切换效果较好的hys和ttt参数，并将其发送到RBF神经网络进行训练，得到不同速度下hys和ttt的非线性表达式，根据列车接收到的参考信号质量，加入自矫正项对hys和ttt进行二次调整和优化。在Matlab上进行的仿真实验结果表明，该算法能够降低掉话率和乒乓切换率，提高列车在高速运行环境下的切换成功率及鲁棒性。

关键词: LTE-R技术, 高速环境, A3事件, RBF神经网络, 切换成功率

CLC Number: 

• TP391